社交媒体用户综合影响力检测方法研究
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摘要
【目的/意义】通过构建影响力评价的检测方法,实现对社交媒体关键用户的识别。【方法/过程】根据关系型网络特点,提出了一种基于最大连通子集、网络连通分支和全局效率的综合检测方法,该方法综合考虑了用户个体在保持网络完整性和连通性方面的作用。【结果/结论】抗毁性实验结果表明:中心性指标适用性与网络结构属性紧密相关。对于稠密网络,个体的介数值越大在网络中所具有的影响力越高;对于稀疏网络而言,个体对外连接度越大其所具有的网络影响力越高。数据来源不够广泛,有可能导致研究偏差。本文所构建的综合检测方法能够有效地判断网络个体影响力的真实水平。
【Purpose/significance】In order to improve the accuracy of the identification indices of key users in online social networks, this paper proposed a comprehensive evaluation method.【Method/process】The comprehensive evaluation method focused on the role of individuals in maintaining network integrity and connectivity, which took into account the giant component, the number of connected components and the global efficiency of the network.【Result/conclusion】The results of the invulnerability test showed that the suitability of different centrality indices was closely related to the network structure properties. In a dense network, the bigger betweenness centrality individuals had, the greater the influence they had; In a sparse network, the bigger degree betweenness centrality individuals had, the greater the influence they had. The collected data was not comprehensive,which might generate some biased results. The paper provides a more effective and accurate method to rank the influence level of individuals in social media.
【Purpose/significance】In order to improve the accuracy of the identification indices of key users in online social networks, this paper proposed a comprehensive evaluation method.【Method/process】The comprehensive evaluation method focused on the role of individuals in maintaining network integrity and connectivity, which took into account the giant component, the number of connected components and the global efficiency of the network.【Result/conclusion】The results of the invulnerability test showed that the suitability of different centrality indices was closely related to the network structure properties. In a dense network, the bigger betweenness centrality individuals had, the greater the influence they had; In a sparse network, the bigger degree betweenness centrality individuals had, the greater the influence they had. The collected data was not comprehensive,which might generate some biased results. The paper provides a more effective and accurate method to rank the influence level of individuals in social media.
引文
1 Gao Q., Abel F., Houben G., et al. A Comparative Study of Users’Microblogging Behavior on Sina Weibo and Twitter[J]. Lecture Notes in Computer Science,2012, 7379:88-101.
2 杨善林,王佳佳,代宝,等.社交媒体用户行为研究现状与展望[J].中国科学院院刊, 2015,(2):200-215.
3 潘骏,沈惠璋,陈忠.基于Agent和K核分解的群体事件微博传播模型[J].情报科学,2018,36(2):125-131.
4 Xia Y.J., Ren X.L., Peng, Z.C., Zhang J.L. Effectively identifying the influential spreaders in large-scale social networks[J]. Multimedia Tools and Applications, 2016,(75):8829-8841.
5 Arruda G. F., Barbieri A. L., Rodriguez P. M., et al.The role of centrality for the identification of influential spreaders in complex networks[J]. Physical Review E,2014,(90):812.
6 阮逸润,老松杨,王竣德,白亮,侯绿林.一种改进的基于信息传播率的复杂网络影响力评估算法[J].物理学报,2017,66(20):208.
7 吴小兰,章成志.基于社交媒体的高影响力跨学科用户发现研究[J].情报学报,2017,36(6):618-627.
8 黄远,沈乾,刘怡君.微博舆论场:突发事件舆情演化分析的新视角[J].系统工程理论与实践,2015,(10):2564-2572.
9 胡吉明.社会网络环境下的信息传播机制[J].情报科学,2013,33(1):15-18.
10 李栋,徐志明,李生,等.在线社会网络中信息扩散[J].计算机学报,2014,37(1):189-206.
11 Kazumi S.,Masahiro K.,Kouzou O.,et al. Super mediator–A new centrality measure of node importance for information diffusion over social network[J]. Information Sciences,2016,(329):985-1000.
12 赵宇,黄开枝,郭云飞,赵星.在线社会网络中面向节点影响力的信息传播阻断模型[J].清华大学学报(自然科学版),2017,57(12):1245-1253.
13 张仰森,郑佳,唐安杰.基于多特征融合的微博用户权威度定量评价方法[J].电子学,2017,45(11):2800-2809.
14 Hou B, Yao Y, Liao D. Identifying all-around nodes for spreading dynamics in complex networks[J]. Physica A,2012,(391):4012-4017.
15 于会,刘尊,李勇军.基于多属性决策的复杂网络节点重要性综合评价方法[J].物理学报,2013,(62):020204.
16 LüL. Y., Zhang Y. C., Yeung C. H., et al. Leaders in social networks, the delicious case[J]. PLoS One, 2011,(6):e21202.
17 Chen D.B., Gao H., Lu L.Y., et al. Identifying influential nodes in large-scale directed networks:The Role of Clustering[J].Plos One, 2013,(8):e77455.
18 王建伟,荣莉莉,郭天柱.一种基于局部特征的网络节点重要性度量方法[J].大连理工大学学报,2010,50(5):822-826.
19 Chen D. B, LüL. Y, Shang M. S, et al. Identifying influential nodes in complex networks[J]. Physica A, 2012,(391):1777-1787.
20 Arruda G.F., Barbieri A.L., Rodriguez P.M., et al. The role of centrality for the identification of influential spreaders in complex networks[J]. Physical Review E, 2014,(90):032812.
21 任晓龙,吕琳媛.网络重要节点排序方法综述[J].科学通报,2014,59(13):1175-1197.
22 Kitsak M., Gallos L. K., Havlin S,et al. Identification of Influential Spreaders in Complex Networks[J]. Nature Physics, 2010, 6(11):888-893.
23 Liu Y.,Tang M., Zhou T.,et al. Improving the accuracy of the k-shell method by removing redundant links:From a perspective of spreading dynamics[J]. Scientific Reports,2015,(5):13172.
24 Xia Y. J., Ren X. L., Peng, Z. C., Zhang J. L. Effectively identifying the influential spreaders in large-scale social networks[J]. Multimedia Tools and Applications, 2016,(75):8829-8841.
25 罗仕龙,龚凯,唐朝生,等.加权网络中基于冗余边过滤的k-核分解排序算法[J].物理学报,2017,66(18):188902.
26 陈福集,陈婷.基于SEIRS传播模型的网络舆情衍生效应研究[J].情报杂志,2014,33(2):108-113,160.
27 Albert R.,JeongH., Barabás A.-L.Error and attack tolerance of complex networks[J].Nature, 2000,(406):378-382.
28 Crucitti P., Latora V., Marchiori M.,Rapisarda A. Error and attack tolerance of complex networks[J]. Physica A, 2004,(340):388-394.
2 杨善林,王佳佳,代宝,等.社交媒体用户行为研究现状与展望[J].中国科学院院刊, 2015,(2):200-215.
3 潘骏,沈惠璋,陈忠.基于Agent和K核分解的群体事件微博传播模型[J].情报科学,2018,36(2):125-131.
4 Xia Y.J., Ren X.L., Peng, Z.C., Zhang J.L. Effectively identifying the influential spreaders in large-scale social networks[J]. Multimedia Tools and Applications, 2016,(75):8829-8841.
5 Arruda G. F., Barbieri A. L., Rodriguez P. M., et al.The role of centrality for the identification of influential spreaders in complex networks[J]. Physical Review E,2014,(90):812.
6 阮逸润,老松杨,王竣德,白亮,侯绿林.一种改进的基于信息传播率的复杂网络影响力评估算法[J].物理学报,2017,66(20):208.
7 吴小兰,章成志.基于社交媒体的高影响力跨学科用户发现研究[J].情报学报,2017,36(6):618-627.
8 黄远,沈乾,刘怡君.微博舆论场:突发事件舆情演化分析的新视角[J].系统工程理论与实践,2015,(10):2564-2572.
9 胡吉明.社会网络环境下的信息传播机制[J].情报科学,2013,33(1):15-18.
10 李栋,徐志明,李生,等.在线社会网络中信息扩散[J].计算机学报,2014,37(1):189-206.
11 Kazumi S.,Masahiro K.,Kouzou O.,et al. Super mediator–A new centrality measure of node importance for information diffusion over social network[J]. Information Sciences,2016,(329):985-1000.
12 赵宇,黄开枝,郭云飞,赵星.在线社会网络中面向节点影响力的信息传播阻断模型[J].清华大学学报(自然科学版),2017,57(12):1245-1253.
13 张仰森,郑佳,唐安杰.基于多特征融合的微博用户权威度定量评价方法[J].电子学,2017,45(11):2800-2809.
14 Hou B, Yao Y, Liao D. Identifying all-around nodes for spreading dynamics in complex networks[J]. Physica A,2012,(391):4012-4017.
15 于会,刘尊,李勇军.基于多属性决策的复杂网络节点重要性综合评价方法[J].物理学报,2013,(62):020204.
16 LüL. Y., Zhang Y. C., Yeung C. H., et al. Leaders in social networks, the delicious case[J]. PLoS One, 2011,(6):e21202.
17 Chen D.B., Gao H., Lu L.Y., et al. Identifying influential nodes in large-scale directed networks:The Role of Clustering[J].Plos One, 2013,(8):e77455.
18 王建伟,荣莉莉,郭天柱.一种基于局部特征的网络节点重要性度量方法[J].大连理工大学学报,2010,50(5):822-826.
19 Chen D. B, LüL. Y, Shang M. S, et al. Identifying influential nodes in complex networks[J]. Physica A, 2012,(391):1777-1787.
20 Arruda G.F., Barbieri A.L., Rodriguez P.M., et al. The role of centrality for the identification of influential spreaders in complex networks[J]. Physical Review E, 2014,(90):032812.
21 任晓龙,吕琳媛.网络重要节点排序方法综述[J].科学通报,2014,59(13):1175-1197.
22 Kitsak M., Gallos L. K., Havlin S,et al. Identification of Influential Spreaders in Complex Networks[J]. Nature Physics, 2010, 6(11):888-893.
23 Liu Y.,Tang M., Zhou T.,et al. Improving the accuracy of the k-shell method by removing redundant links:From a perspective of spreading dynamics[J]. Scientific Reports,2015,(5):13172.
24 Xia Y. J., Ren X. L., Peng, Z. C., Zhang J. L. Effectively identifying the influential spreaders in large-scale social networks[J]. Multimedia Tools and Applications, 2016,(75):8829-8841.
25 罗仕龙,龚凯,唐朝生,等.加权网络中基于冗余边过滤的k-核分解排序算法[J].物理学报,2017,66(18):188902.
26 陈福集,陈婷.基于SEIRS传播模型的网络舆情衍生效应研究[J].情报杂志,2014,33(2):108-113,160.
27 Albert R.,JeongH., Barabás A.-L.Error and attack tolerance of complex networks[J].Nature, 2000,(406):378-382.
28 Crucitti P., Latora V., Marchiori M.,Rapisarda A. Error and attack tolerance of complex networks[J]. Physica A, 2004,(340):388-394.